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Epidemic Threshold in Temporally-Switching Networks

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Temporal Network Epidemiology

Part of the book series: Theoretical Biology ((THBIO))

Abstract

Infectious diseases have been modelled on networks that summarise physical contacts or close proximity of individuals. These networks are known to be complex in both their structure and how they change over time. We present an overview of recent progress in numerically determining the epidemic threshold in temporally-switching networks, and illustrate that slower switching of snapshots relative to epidemic dynamics lowers the epidemic threshold. Therefore, ignoring the temporally-varying nature of networks may underestimate endemicity. We also identify a predictor for the magnitude of this shift which is based on the commutator norm of snapshot adjacency matrices.

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Acknowledgements

LS acknowledges the support provided through the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/G03706X/1]. LS and NM acknowledge the support provided through JST, ERATO, Kawarabayashi Large Graph Project. KK acknowledges funding from the Ramón y Cajal program of MINECO. KK and VME acknowledge support from projects SPASIMM (FIS2016-80067-P AEI/FEDER, UE) and NOMAQ (FIS2014-60343-P). NM acknowledges the support provided through JST, CREST.

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Authors and Affiliations

  1. Department of Statistics, University of Oxford, 24–29 St Giles, OX1 3LB, Oxford, UK

    Leo Speidel

  2. Doctoral Training Centre in Systems Biology, University of Oxford, Rex Richards Building, South Parks Road, OX1 3QU, Oxford, UK

    Leo Speidel

  3. School of Science and Technology, Nazarbayev University, Qabanbay BatyrAve 53, 010000, Astana, Kazakhstan

    Konstantin Klemm

  4. Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), E07122, Palma de Mallorca, Spain

    Konstantin Klemm & Víctor M. Eguíluz

  5. Department of Engineering Mathematics, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Clifton, Bristol, UK

    Naoki Masuda

Authors
  1. Leo Speidel
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  2. Konstantin Klemm
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  3. Víctor M. Eguíluz
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  4. Naoki Masuda
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Corresponding author

Correspondence to Naoki Masuda .

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Editors and Affiliations

  1. Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom

    Naoki Masuda

  2. Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan

    Petter Holme

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Speidel, L., Klemm, K., Eguíluz, V.M., Masuda, N. (2017). Epidemic Threshold in Temporally-Switching Networks. In: Masuda, N., Holme, P. (eds) Temporal Network Epidemiology. Theoretical Biology. Springer, Singapore. https://doi.org/10.1007/978-981-10-5287-3_7

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